| Mutilateral Environmental Agreements 1857 - 2016 | ||||
|---|---|---|---|---|
| mitch_id | Year signed | Year ratified | Subject | Lineage |
| 2584 | 1877 | 1877 | Species | Rhine - Lake Constance Fishing |
| 2585 | 1878 | 1880 | Species | International Plant Protection |
| 2586 | 1881 | 1882 | Species | International Plant Protection |
| 2587 | 1884 | 2899 | Species | Rhine - Lake Constance Fishing |
| 2227 | 1885 | 1886 | Freshwater | Congo River Basin |
| 2588 | 1885 | 1886 | Species | Rhine - Lake Constance Fishing |
| 773 | 1990 | 1992 | Freshwater | Elbe Pollution |
| 817 | 1991 | 1993 | Freshwater | Elbe Pollution |
| 2489 | 1992 | 1992 | General | CIS Environmental Agreements |
| 1556 | 2000 | 2006 | Freshwater | Danube |
| Data from Ronald B. Mitchell. 2002-2020. International Environmental Agreements Database Project | ||||
dp3 <- data %>%
select("subject_category", "tsig_yr", "teif_yr") %>%
mutate(year_gap = teif_yr - tsig_yr) %>%
# filter(year_gap > 100)
filter(year_gap < 100) %>%
group_by(year_gap) %>% # 349 year_gap = 0
count() %>%
summary(avg_gap = mean(year_gap)) #???
# mean 2.236
sameyear = function(...) return(349)
meanyear = function(...) return(2.2)
never = function(...) return(141)[1] 1311
# consent to be bound 448/1311
data %>%
select(t_more_research, t_tap_clause1, region_type, region_continent, eif_type1) %>%
group_by(eif_type1) %>%
count(eif_type1)# A tibble: 8 x 2
# Groups: eif_type1 [8]
eif_type1 n
<chr> <int>
1 "" 745
2 "#N/A" 8
3 "Consent to be bound" 448
4 "EIF absent" 2
5 "Follows agreement" 1
6 "Missing agreement text" 2
7 "On signature" 103
8 "Tacit" 2
# Politically adaptive 36
data %>%
select(t_more_research, t_tap_clause1, region_type, dynamic, eif_type1) %>%
group_by(dynamic) %>%
count(dynamic)# A tibble: 6 x 2
# Groups: dynamic [6]
dynamic n
<chr> <int>
1 "" 20
2 "#N/A" 737
3 "Permanent" 296
4 "Politically adaptive" 36
5 "Substantively adaptive" 192
6 "Unsure" 30
# Regular updating needed 264
data %>%
select(t_more_research, t_tap_clause1, region_type, region_continent, eif_type1) %>%
group_by(t_more_research) %>%
count(t_more_research)# A tibble: 6 x 2
# Groups: t_more_research [6]
t_more_research n
<chr> <int>
1 "" 20
2 "Incomplete information" 10
3 "Low priority" 131
4 "Regular updating needed" 264
5 "Uncertain action information" 27
6 "Update not needed" 859